Optimization of settlement land use through carbon footprint approach in The North Balikpapan
Bibliographic record
Abstract
Abstract Limited land in the downtown area as well as the increasing amount of new activities centre causes residential development leads to North Balikpapan. This area is an urban fringe with vast protected forests as buffer zone and catchment area for the city and surrounding area. Land conversion in this area will increase hazard risk of inundation, water quality decrease and increased CO2 emissions. Therefore, development should be maintained environmental stability. One of the rights applicated approach is carbon footprint that is capable to measure the balance between production and absorption needs of CO2 emissions. To find the optimal land allocation, we used carbon footprint calculation from the household activities, identify the factors of settlement growth, and use Linear Programming analysis. Analysis’ results show that settlement activities in North Balikpapan produce 108.362,4 tCO2/year or equivalent with 618,50 Ha green space. Meanwhile, the development of settlement in North Balikpapan is affected by social demographic, developer initiative, environmental condition, public facilities availability, economical structure, and policy factors. According to those factors, optimal allocation of settlement area in North Balikpapan is only about 4,510.01 Ha. With that condition, it still able to absorb CO2 emissions from inside or outside the area around 2.751 tCO2/year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".